@article{negi:BinaryOperationalizingReceptivity-2026,
    author = "Negi, Samarth and Mishra, Varun and Kum, Chai Yin and Castro, Oscar and Kowatsch, Tobias and Mair, Jacqueline L. and {von Wangenheim}, Florian",
    title = "Beyond the {{Binary}}: {{Operationalizing Receptivity}} to {{Digital Health Interventions}} as a {{Time}}-to-{{Event Spectrum}}",
    shorttitle = "Beyond the {{Binary}}",
    year = "2026",
    month = "September",
    journal = "Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. (IMWUT)",
    volume = "10",
    number = "3",
    pages = "141:1--141:27",
    doi = "10.1145/3832017",
    abstract = "Just-In-Time Adaptive Interventions (JITAIs) aim to support health behavior by providing the right support at the right time. A critical determinant of JITAI efficacy is timing delivery such that the user is receptive, defined as the cognitive and behavioral capacity to receive, process, and use support. While prior work has explored context sensing to predict receptivity, standard approaches typically operationalize this construct as a binary outcome within a fixed window, despite theoretical definitions characterizing availability as a continuous, time-varying state. Modeling receptivity at this granularity increases learning complexity, and deep sequence models are further constrained by the scarcity of labeled interaction data in mHealth settings. To address these challenges, we propose PRISM, a deep learning framework for modeling receptivity as a probabilistic time-to-event distribution from longitudinal mobile sensing data. PRISM combines a Channel-Independent Transformer (PatchTST) encoder with a discrete-time survival objective and employs self-supervised pre-training on unlabeled sensor traces via Masked Patch Reconstruction to mitigate label scarcity.; AB@We evaluate PRISM on the LvL UP intervention dataset, leveraging data from preliminary studies for pre-training and a large-scale efficacy trial for evaluation. Our results demonstrate competitive performance with established receptivity benchmarks, with self-supervised initialization yielding up to 15.5\\% improvement in median AUC across heterogeneous user groups. Beyond single-window evaluation, PRISM remains stable across multiple decision horizons from a single trained model, in contrast to binary baselines that degrade or require retraining at each cutoff. A follow-up evaluation on an independent dataset with variable prompt timing provides preliminary evidence that the learned representations transfer across cohorts and schedules. These findings suggest that PRISM provides a data-efficient pathway for deploying resilient, time-aware receptivity models in real-world mHealth systems."
}